Power grid flood prevention rapid disposal method based on multi-dimensional data

Abstract

The invention relates to the technical field of power grid flood prevention, in particular to a power grid flood prevention rapid disposal method based on multi-dimensional data. Comprising the following steps: acquiring meteorological data, hydrological data, power grid equipment information, power grid map information, first-aid repair vehicle information, first-aid repair team information, material information, power grid equipment fault information, power failure information and fault area information of a current region; establishing a power grid equipment risk library; grid division is carried out; and in combination with meteorological data, power grid equipment fault information, power failure information and fault area information, a first-aid repair team is determined according to the distance and the weight by taking the coordinates of the power grid equipment with the fault as the center, and an optimal first-aid repair route is given. According to the invention, early warning can be carried out on power grid equipment faults in a flood season according to rainfall forecast information, personnel and materials are flexibly allocated, an optimal first-aid repair route is given, the first-aid repair efficiency of the power grid equipment is greatly improved, and cost reduction, quality improvement and efficiency improvement are realized.

Description

Technical Field

[0001] The invention relates to the technical field of power grid flood prevention, and in particular to a power grid flood prevention rapid disposal method based on multi-dimensional data. Background Art

[0002] With the frequent occurrence of abnormal climate events, such as heavy rains, floods and other extreme weather conditions, power equipment has been damaged and shut down, and failures have occurred frequently, posing a severe test to the power supply.

[0003] Existing power repair methods have many shortcomings in dealing with sudden failures. They mainly rely on the temporary deployment of existing materials, personnel and repair routes after a failure occurs. The response speed is slow and the resource utilization efficiency is low. In addition, the means of deploying materials and personnel, and planning routes for repair vehicles such as generators are single and lack accuracy and flexibility. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a rapid flood prevention and disposal method for power grids based on multi-dimensional data. During the flood season, according to the forecast rainfall information, early warning of power grid equipment failures can be issued, and personnel and materials can be flexibly deployed to provide the optimal repair route, thereby greatly improving the repair efficiency of power grid equipment and achieving cost reduction, quality improvement and efficiency increase.

[0005] The technical solution adopted by the present invention is to provide a method for rapid flood prevention and disposal of power grids based on multi-dimensional data, including obtaining meteorological data, hydrological data, power grid equipment information, power grid map information, emergency repair vehicle information, emergency repair team information, material information, power grid equipment failure information, power outage information, and failure area information of the current region;

[0006] Integrate meteorological data and hydrological data, divide them into risk areas according to rainfall, combine power grid equipment information and power grid map information, use quadtree algorithm to establish a power grid equipment risk library for power grid equipment in risk areas;

[0007] According to the grid equipment coordinates in the grid equipment risk database, combined with the repair vehicle information, repair team information, and material information, grid division is performed;

[0008] Weights are allocated to repair vehicles, repair teams and materials. Combined with meteorological data, power grid equipment failure information, power outage information and fault area information, a neighborhood search algorithm is used in the grid area with the coordinates of the faulty power grid equipment as the center to search for repair teams around the fault point. The repair team is determined based on the distance and weight, and the optimal repair route is given.

[0009] The meteorological data includes real-time rainfall information and forecast rainfall information, and the hydrological data includes rivers, reservoirs, flood storage areas, flood discharge channels and inundated areas.

[0010] Based on the forecast rainfall information, the power grid equipment risk database is updated in real time.

[0011] The power grid equipment information includes equipment coordinate information, and the power grid one-picture information includes power grid topology, equipment information and equipment business operation data.

[0012] The risk areas include high-risk areas, medium-risk areas and low-risk areas.

[0013] Weights are allocated to repair vehicles, repair teams and materials based on the quantity of materials, vehicle performance, attendance rate, staffing of the repair team, professionalism of the repair personnel, and the load of the repair personnel.

[0014] The real-time coordinates of the repair vehicles and repair personnel form coordinate points on a power grid map.

[0015] The beneficial effect of the present invention is that it provides a rapid flood prevention and disposal method for power grids based on multi-dimensional data. During the flood season, it integrates meteorological data, hydrological data, power grid equipment information, and power grid map information. When the rainfall is too heavy, it predicts the scope of the area that may be flooded, gives early warning information to the power grid equipment, and flexibly and accurately deploys emergency repair vehicles, repair teams and materials to quickly repair faulty power grid equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0017] like Figure 1 As shown, the present invention provides a method for rapid flood prevention of power grid based on multi-dimensional data, and the specific implementation steps are as follows:

[0018] Step 1: Obtain meteorological data, including real-time rainfall information and forecast rainfall information, obtain hydrological data including rivers, reservoirs, flood storage areas, flood discharge channels, and inundated areas, and obtain power grid equipment information, including power grid equipment coordinate information.

[0019] Step 2: Obtain a picture of the power grid, including power grid topology, equipment information, and equipment business operation data.

[0020] Step 3: Obtain the repair vehicle information, including the real-time coordinates and operating load of the repair vehicle; obtain the repair team information, including the repair position, the number of repair team members, and the repair team load; obtain the material information, including the material warehouse location, material type, and material equipment quantity.

[0021] Step 4: Obtain grid equipment fault information, power outage information, and fault area information, where the fault area information includes street and community information.

[0022] Step 5: Data processing: integrating meteorological data and hydrological data, dividing the risk areas into three levels: low risk, medium risk, and high risk according to the current rainfall and the forecast rainfall for the next 30 minutes, 1 hour, 3 hours, 12 hours, and 24 hours, as well as the hydrological information within the geographical area.

[0023] Step 6: Based on the risk area and the power grid equipment information, the quadtree algorithm is used: first determine the scope of the working area, that is, the space to decompose the current node into 2d child nodes, where d is the dimension of the space, so that each node contains the root node of the quadtree.

[0024] When inserting spatial objects into a spatial database, if the number of spatial objects in a node reaches a certain threshold, the number of spatial objects required is less than the given threshold.

[0025] Its insertion algorithm can be described as follows:

[0026] (1) Calculate the MBR of the spatial object, i.e. the minimum bounding rectangle;

[0027] (2) Through the root node R, search for all leaf nodes that contain the object;

[0028] (3) Determine whether the number of spatial objects contained in these leaf nodes exceeds a threshold;

[0029] (4) If the number of objects in the node exceeds the threshold, decompose the current node into 2d;

[0030] (5) For the newly generated nodes, recalculate the spatial objects that fall into them;

[0031] (6) Repeat (1) to (5);

[0032] Finally, based on the coordinates of the power grid equipment, a power grid equipment risk database is formed, that is, an information database of power grid equipment with failure risks.

[0033] Step 7: Grid the grid equipment coordinates, repair vehicle information, repair team, and material information involved in the grid equipment risk database, and divide them into small grids according to geographical locations, such as districts, streets, and communities. Taking a city as an example, it is divided into several district grids according to the districts, and then the boundary inspection method is used to determine which district grid it belongs to based on the equipment coordinates in the grid equipment risk database and the repair vehicle, repair team, and material information, forming a grid management system model.

[0034] Step 8: Combine meteorological data to update the power grid equipment risk database in real time.

[0035] Step 9. According to the grid management system model, weights are assigned to the repair team, vehicles, and material information, which are divided into high, medium, and low. The weights are assigned based on the amount of materials in the material warehouse, the performance and attendance rate of the vehicles, the staffing of the repair team, the professionalism of the repair personnel, the load of the repair personnel, and the attendance rate. The higher the weight priority, the repair team will be dispatched first, the vehicle will be used first, and the materials will be used first. The medium priority will be dispatched next, and the low priority will be dispatched last. A deployment model library is formed.

[0036] Step 10: During the flood season, the optimal repair route is given by combining real-time rainfall information, user power outage information, and faulty equipment information through a grid management model, a grid map, and grid topology. The fault point is taken as the center point, and a neighborhood search algorithm is used within the grid area of ​​the fault point to search for the team closest to the fault point with a high priority.

[0037] Step 11: Through the optimal route and deployment model library provided by the power grid equipment fault information and the grid management model, a repair organization diagram including information such as power grid topology and equipment status is formed to support the efficient implementation of distribution network repair work in the disaster-stricken areas and provide intuitive information support for repair combat command and resource dispatch.

[0038] Step 12: At the same time, the coordinates of the repair vehicles and repair personnel are formed into coordinate points on a power grid map in real time, which is convenient for on-site management and understanding of the progress of on-site repairs.

[0039] The present invention takes power grid equipment and facilities as the main body, integrates multiple information elements such as geography, meteorology, and hydrology, carries out flood prevention forecasting and early warning work for power grid equipment, forms a forecast simulation of areas that may be submerged due to flood discharge caused by rainfall, and supports operation and maintenance guarantee and emergency response during disasters, as well as post-disaster transformation and recovery and reconstruction work.

Claims

1. A rapid flood prevention method for power grid based on multi-dimensional data, characterized in that: Including obtaining the current regional meteorological data, hydrological data, power grid equipment information, power grid map information, emergency repair vehicle information, emergency repair team information, material information, power grid equipment failure information, power outage information, and fault area information; Integrate meteorological data and hydrological data, divide them into risk areas according to rainfall, combine power grid equipment information and power grid map information, use quadtree algorithm to establish a power grid equipment risk library for power grid equipment in risk areas; According to the grid equipment coordinates in the grid equipment risk database, combined with the repair vehicle information, repair team information, and material information, grid division is performed; Weights are allocated to repair vehicles, repair teams and materials. Combined with meteorological data, power grid equipment failure information, power outage information and fault area information, a neighborhood search algorithm is used in the grid area with the coordinates of the faulty power grid equipment as the center to search for repair teams around the fault point. The repair team is determined based on the distance and weight, and the optimal repair route is given.

2. A method for rapid flood prevention in power grid based on multi-dimensional data according to claim 1, characterized in that: The meteorological data includes real-time rainfall information and forecast rainfall information, and the hydrological data includes rivers, reservoirs, flood storage areas, flood discharge channels and inundated areas.

3. A method for rapid flood prevention in power grid based on multi-dimensional data according to claim 2, characterized in that: Based on the forecast rainfall information, the power grid equipment risk database is updated in real time.

4. A method for rapid flood prevention of power grid based on multi-dimensional data according to claim 1, characterized in that: The power grid equipment information includes equipment coordinate information, and the power grid one-picture information includes power grid topology, equipment information and equipment business operation data.

5. The method for rapid flood prevention of power grid based on multi-dimensional data according to claim 1, characterized in that: The risk areas include high-risk areas, medium-risk areas and low-risk areas.

6. A method for rapid flood prevention in power grid based on multi-dimensional data according to claim 1, characterized in that: Weights are allocated to repair vehicles, repair teams and materials based on the quantity of materials, vehicle performance, attendance rate, staffing of the repair team, professionalism of the repair personnel, and the load of the repair personnel.

7. A method for rapid flood prevention in power grid based on multi-dimensional data according to claim 1, characterized in that: The real-time coordinates of the repair vehicles and repair personnel form coordinate points on a power grid map.

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